Efficient Soft-Constrained Clustering for Group-Based Labeling
Efficient Soft-Constrained Clustering for Group-Based Labeling
复制标题
用于基于组的标记的高效软约束聚类
DOI：
10.1007/978-3-030-32254-0_47
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
and S. Uchida
中科院分区：
文献类型：
--
作者：
R. Bise;K. Abe;H. Hayashi;K. Tanaka;and S. Uchida
我们提出了一种软约束聚类方法，用于基于组的医学图像标记。由于基于组的标记的想法是将标签一次性附加到一组样本上，因此我们需要具有组（即，簇）具有高纯度。所提出的方法制定，以实现高纯度，即使是困难的聚类任务，如医学图像聚类，其中同一类的图像样本往往是非常遥远的特征空间。事实上，这些图像降低了传统约束聚类方法的性能。内窥镜图像数据集的实验表明，我们的方法优于各种国家的最先进的方法。
We propose a soft-constrained clustering method for group-based labeling of medical images. Since the idea of group-based labeling is to attach the label to a group of samples at once, we need to have groups (i.e., clusters) with high purity. The proposed method is formulated to achieve high purity even for difficult clustering tasks such as medical image clustering, where image samples of the same class are often very distant in their feature space. In fact, those images degrade the performance of conventional constrained clustering methods. Experiments with an endoscopy image dataset demonstrated that our method outperformed various state-of-the-art methods.